Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

2.  final def ##(): Int

   Definition Classes

   AnyRef → Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

4.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

5.  def buildReader(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

   Returns a function that can be used to read a single file in as an Iterator of InternalRow.

   Returns a function that can be used to read a single file in as an Iterator of InternalRow.

   dataSchema

   The global data schema. It can be either specified by the user, or reconciled/merged from all underlying data files. If any partition columns are contained in the files, they are preserved in this schema.

   partitionSchema

   The schema of the partition column row that will be present in each PartitionedFile. These columns should be appended to the rows that are produced by the iterator.

   requiredSchema

   The schema of the data that should be output for each row. This may be a subset of the columns that are present in the file if column pruning has occurred.

   filters

   A set of filters than can optionally be used to reduce the number of rows output

   options

   A set of string -> string configuration options.

   Attributes

   protected

   Definition Classes

   FileFormat

6.  def buildReaderWithPartitionValues(sparkSession: SparkSession, dataSchema: StructType, partitionSchema: StructType, requiredSchema: StructType, filters: Seq[Filter], options: Map[String, String], hadoopConf: Configuration): (PartitionedFile) ⇒ Iterator[InternalRow]

   Build the reader.

   Build the reader.

   Definition Classes

   ParquetFileFormat → FileFormat

   Note

   It is required to pass FileFormat.OPTION_RETURNING_BATCH in options, to indicate whether the reader should return row or columnar output. If the caller can handle both, pass FileFormat.OPTION_RETURNING_BATCH -> supportBatch(sparkSession, StructType(requiredSchema.fields ++ partitionSchema.fields)) as the option. It should be set to "true" only if this reader can support it.

7.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

8.  final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

9.  def equals(other: Any): Boolean

   Definition Classes

   ParquetFileFormat → AnyRef → Any

10.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

11.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

12.  def hashCode(): Int

    Definition Classes

    ParquetFileFormat → AnyRef → Any

13.  def inferSchema(sparkSession: SparkSession, parameters: Map[String, String], files: Seq[FileStatus]): Option[StructType]

    When possible, this method should return the schema of the given files.

    When possible, this method should return the schema of the given files. When the format does not support inference, or no valid files are given should return None. In these cases Spark will require that user specify the schema manually.

    Definition Classes

    ParquetFileFormat → FileFormat

14.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    Attributes

    protected

    Definition Classes

    Logging

15.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

16.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

17.  def isSplitable(sparkSession: SparkSession, options: Map[String, String], path: Path): Boolean

    Returns whether a file with path could be split or not.

    Returns whether a file with path could be split or not.

    Definition Classes

    ParquetFileFormat → FileFormat

18.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

19.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

20.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

21.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

22.  def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

23.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

24.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

25.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

26.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

27.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

28.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

29.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

30.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

31.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

32.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

33.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

34.  def prepareWrite(sparkSession: SparkSession, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory

    Prepares a write job and returns an OutputWriterFactory.

    Prepares a write job and returns an OutputWriterFactory. Client side job preparation can be put here. For example, user defined output committer can be configured here by setting the output committer class in the conf of spark.sql.sources.outputCommitterClass.

    Definition Classes

    ParquetFileFormat → FileFormat

35.  def shortName(): String

    The string that represents the format that this data source provider uses.

    The string that represents the format that this data source provider uses. This is overridden by children to provide a nice alias for the data source. For example:

    override def shortName(): String = "parquet"

    Definition Classes

    ParquetFileFormat → DataSourceRegister

    Since

    1.5.0

36.  def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean

    Returns whether the reader can return the rows as batch or not.

    Returns whether the reader can return the rows as batch or not.

    Definition Classes

    ParquetFileFormat → FileFormat

37.  def supportDataType(dataType: DataType): Boolean

    Returns whether this format supports the given DataType in read/write path.

    Returns whether this format supports the given DataType in read/write path. By default all data types are supported.

    Definition Classes

    ParquetFileFormat → FileFormat

38.  def supportFieldName(name: String): Boolean

    Returns whether this format supports the given filed name in read/write path.

    Returns whether this format supports the given filed name in read/write path. By default all field name is supported.

    Definition Classes

    FileFormat

39.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

40.  def toString(): String

    Definition Classes

    ParquetFileFormat → AnyRef → Any

41.  def vectorTypes(requiredSchema: StructType, partitionSchema: StructType, sqlConf: SQLConf): Option[Seq[String]]

    Returns concrete column vector class names for each column to be used in a columnar batch if this format supports returning columnar batch.

    Returns concrete column vector class names for each column to be used in a columnar batch if this format supports returning columnar batch.

    Definition Classes

    ParquetFileFormat → FileFormat

42.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

43.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

44.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()